• Journal of Korean Society for Geospatial Information Science
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• v.24 no.2
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• pp.37-46
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• 2016

The difference between definition time of GPS (Global Positioning System) position data and actual display time of car positions on a map could reduce the accuracy of car positions displayed in PND (Portable Navigation Device)-type CNS (Car Navigation System). Due to the time difference, the position of the car displayed on the map is not its current position, so an improved method to fix these problems is required. It is expected that a method that uses predicted future positionsto compensate for the delay caused by processing and display of the received GPS signals could mitigate these problems. Therefore, in this study an analysis was conducted to correct late processing problems of map positions by mapmatching using a Kalman filter with only GPS position data and a RRF (Road Reduction Filter) technique in a light-weight CNS. The effects on routing services are examined by analyzing differences that are decomposed into along and across the road elements relative to the direction of advancing car. The results indicate that it is possible to improve the positional accuracy in the along-the-road direction of a light-weight CNS device that uses only GPS position data, by applying a Kalman filter and RRF.

• The KIPS Transactions:PartC
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• v.13C no.1 s.104
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• pp.95-102
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• 2006

Nowadays, GPS is used widely, especially in case which needs precise position information, such as car navigation systems and various kinds of position measuring instruments in an outdoor environment. According to their applications, there are many kinds of GPS receivers with different costs and error rates. The maximum error range of the general-purpose GPS receiver is within 30m, though the error rate depends on receiving rate of signal and weather condition. RTK(Real-Time Kinematic) and DGPS(Differential Global Positioning System) have more precise accuracy than the general-purpose GPS. However end users can't afford use them because of their high price and large size of equipments. In order for the end user to obtain precise position information, it is important that GPS receivers has portability and low price. In this study, we introduce a new system that offers precise position information using the DGPS mechanism satisfying low cost and portability.

• The Journal of Korea Robotics Society
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• v.11 no.3
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• pp.127-132
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• 2016

For a practical mobile robot team such as carrying out a search and rescue mission in a disaster area, the localization have to be guaranteed even in an environment where the network infrastructure is destroyed or a global positioning system (GPS) is unavailable. The proposed architecture supports localizing robots seamlessly by finding their relative locations while moving from a global outdoor environment to a local indoor position. The proposed schemes use a cooperative positioning system (CPS) based on the two-way ranging (TWR) technique. In the proposed TWR-based CPS, each non-localized mobile robot act as tag, and finds its position using bilateral range measurements of all localized mobile robots. The localized mobile robots act as anchors, and support the localization of mobile robots in the GPS-shadow region such as an indoor environment. As a tag localizes its position with anchors, the position error of the anchor propagates to the tag, and the position error of the tag accumulates the position errors of the anchor. To minimize the effect of error propagation, this paper suggests the new scheme of full-mesh based CPS for improving the position accuracy. The proposed schemes assuring localization were validated through experiment results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.5
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• pp.1400-1420
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• 2012

Object detection and tracking using visual sensors is a critical component of surveillance systems, which presents many challenges. This paper addresses the enhancement of object detection and tracking via the combination of multiple visual sensors. The enhancement method we introduce compensates for missed object detection based on the partial detection of objects by multiple visual sensors. When one detects an object or more visual sensors, the detected object's local positions transformed into a global object position. Local and global information exchange allows a missed local object's position to recover. However, the exchange of the information may degrade the detection and tracking performance by incorrectly recovering the local object position, which propagated by false object detection. Furthermore, local object positions corresponding to an identical object can transformed into nonequivalent global object positions because of detection uncertainty such as shadows or other artifacts. We improved the performance by preventing the propagation of false object detection. In addition, we present an evaluation method for the final global object position. The proposed method analyzed and evaluated using case studies.

• Smart Structures and Systems
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• v.7 no.5
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• pp.349-363
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• 2011

To assess the performance of a structure requires the measurement of global and relative displacements at critical points across the structure. They should be obtained in real time and in all weather condition. A Global Navigation Satellite System (GNSS) could satisfy the last two requirements. The American Global Position System (GPS) provides long term acquisitions with sampling rates sufficient to track the displacement of long period structures. The accuracy is of the order of sub-centimetres. The steel building which hosts the authors' laboratory is the reference case-study within this paper. First a comparison of data collected by GPS sensor units with data recorded by tri-axial accelerometers is carried out when dynamic vibrations are induced in the structure by movements of the internal bridge-crane. The elaborations from the GPS position readings are then compared with the results obtained by a Finite Element (FE) numerical simulation. The purposes are: i) to realize a refinement of the structural parameters which characterize the building and ii) to outline a suitable way for processing GPS data toward structural monitoring.

• Journal of Aerospace System Engineering
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• v.4 no.3
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• pp.17-23
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• 2010

GPS sensors provide accurate position and velocity of moving vehicles. However, GPS is weak at intermittent signal loss and large position error. Combination with INS improves the GPS position accuracy during the GPS signal loss. In this paper, a fusion filter using GPS and INS is developed and its perfomance is analyzed with RC car and RC airplane experiments.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.2
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• pp.97-103
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• 2005

Most car navigation systems(CNS) estimate the vehicle's location using global positioning system(GPS) or dead reckoning(DR) system. However, the estimated location has undesirable errors because of various noise sources such as unpredictable GPS noises. As a result, the measured position is not lying on the road, although the vehicle is known to be restricted on the road network. The purpose of map matching is to locate the vehicle's position on the road network where the vehicle is most likely to be positioned. In this paper, we analyze some general map matching algorithms first. Then, we propose a map matching method using compensation vectors to improve the performance of map matching. The proposed method calculates a compensation vector from the discrepancy between a measured position and an estimated position. The compensation vector and a newly measured position are to be used to determine the next estimation. To show the performance improvement of the map matching using compensation vectors, the real time map matching experiments are performed. The real road experiments demonstrate the effectiveness and applicability of the proposed map matching.

• Journal of Astronomy and Space Sciences
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• v.18 no.1
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• pp.63-70
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• 2001

IDGPS(Inverted Differential Global Positioning System) is one of technique improving the accuracy of GPS positioning and is mostly used for tracking an automatic vehicle. In the IDGPS, the user send it’s GPS position and related satellite information to dispatcher, and the corrections are made at the dispatcher to get corrected user position. IDGPS suffered correction degradation as the baseline become large. This problem is resolved using NIDGPS(Network IDGPS). As the experimental results are demonstrated, the improvement of position accuracy using IDGPS and NIDGPS is verified.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.33 no.12
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• pp.101-108
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• 2005

The GPS(Global Positioning System) receiver for satellite launch vehicles which will be mounted on a launch vehicle can be applied to the flight safety system with its accurately calculated position and velocity data during vehicle's flight. This paper analyzes the performance of the GPS receiver such as SNR(Signal to Noise Ratio), fix mode, position and velocity error, number of visible and tracking satellites, and PDOP(Position Dilution of Precision) under temperature variation which is changed from -34$^{\circ}C$ to +71$^{\circ}C$.

• Journal of Advanced Navigation Technology
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• v.20 no.5
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• pp.433-439
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• 2016

Several low-cost global navigation satellite system (GNSS) receivers do not support general range-domain correction, and DGNSS-CP (differential GNSS) method had been suggested to solve this problem. It improves its position accuracy by projecting range-domain corrections to the position-domain and then differentiating the stand-alone position by the projected correction. To project the range-domain correction, line-of-sight vectors from the receiver to each satellite should be calculated. The line-of-sight vectors can be obtained from GNSS broadcast ephemeris data or satellite direction information, and this paper shows positioning performance for the two methods. Stand-alone positioning result provided from Septentrio PolaRx4 Pro receiver was used to show the difference. The satellite direction information can reduce the computing load for the DGNSS-CP by 1/15, even though its root mean square(RMS) of position error is bigger than that of ephemeris data by 0.1m.